1

Cyclical dynamics of NI House Prices

Understanding Market CyclesProf. T.V Grissom, Dr. M. McCord, Dr. P.T Davis, Prof.

W.S. McGreal & Dr. D. McIlhatton

ERES Conference 2012 – Edinburgh

2

2.Recessionary Environment?

NICE decade

RUDE Awakening

3

3.Property: a ubiquitous crisis factor?

4

4. Collapse more severe in NI

5

5.Rationale• Limited empirical analysis investigating stylised facts of the

lead-lag relationships• Do any transmittal and endogenous shocks exist?• Is there interdependence or segmentation between

different housing types in NI?• Do price movements in price transmission play a leading

role in one type?• Assuming expectations of sub-sector price patterns this

paper attempts to investigate endogenous time series components by:– focusing on the trends and cyclical relationships

• concentrating on temporal lag patterns and autoregressive structures

6

6.Previous research • Holly et al. (2011) – price shock in London diffuses

through the housing system & is highly co-integrated using generalised impulse response functions

• Stevenson (2004) applied pairwise & multivariate co-integration plus Granger-causality tests– Close association between HM areas – instances of Granger

causality• Chen et al. (2011) HP indices in Taiwan using generalised

forecast error variance decomposition & impulse response– Bi-directional relationship HP in economic centre &

surrounding hinterland – no Granger-cause

7

7.Previous research • Oikarinen (2006) – Johansen testing & VECM examining

lead-lag price ripples– Price diffusion follows classic central place but also indicative

of price change stemming from peri-urban to urban core• Luo et al. (2007) – VECM

– One –directional cause between Sydney and Melbourne – hierarchy of price transmission

• Tu (2000) limited Granger-causality between capital cities – ripple effect evident

• Costello et al. (2011) – capital city HM’s segmented with regards to transmission of non-fundamental shocks

8

8.Previous research • Lee Kwang-tack (1996) – HP affect money supply through

Granger-causality testing• Igan et al (2011) applying dynamic generalised factor model &

spectral approaches– HP cyclicity and transmissions between residential investment,

credit & IR’s– Stylised facts in that HP cyclicity leads credit & real activity over L-T;

findings varied over S-T– IR’s tend to lag all other cycles at all time horizons

• Rossini (2002) – testing price indices for regimes and cyclicity– Prices appear cyclical & consistent across dwellings– In terms of growth rate – price high variability and non-stationary – Suggestive of local demand and supply tastes & lead-lag structure

across housing types

9

9.Previous Research • Hon-Chung Hui, (2011) - cyclical dynamics of

landed and non-landed housing sub-markets in Malaysia– Granger-causality and impulse response

• condominium price cycles lead the price cycles in other market sub-sectors by one to two quarters and predict the price diffusion across particular sub-sector markets

• Chinloy (1996) Tuscan HM – sensitive to periodicity of cyclic behaviour – lags of 3 years

• Grissom & DeLisle (1999) cyclic fluctuation is characterised by time delineated regimes

10

10.NI v UK House Prices – The Cause? Cycles?

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20110

50000

100000

150000

200000

250000

300000 NI House Price UK House Price

11

11.HP Growth Cycle and Projection

0

50000

100000

150000

200000

250000

300000

f(x) = 1663.26975023127 x + 30681.4869565217R² = 0.980777334182851

UUHPI (1995Q1 – 2011Q2)Collated Q basis based on sale priceDisaggregated by Property Type

12

12. NI House Price Movements

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

1996 1998 2000 2002 2004 2006 2008 2010 2012

APART DET_BUNG DET_HOUSESD_BUNG SD_HOUSE TERRACE

13

13.Market Change (%) Correlation

-.6

-.4

-.2

.0

.2

.4

.6

.8

1996 1998 2000 2002 2004 2006 2008 2010 2012

CHG_APART CHG_DET_BUNGCHG_DET_HOUSE CHG_SD_BUNGCHG_SD_HOUSE CHG_TERR

14

14.Market Change (%) Type

-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

1996 1998 2000 2002 2004 2006 2008 2010 2012

CHG_APART

-.3

-.2

-.1

.0

.1

.2

.3

1996 1998 2000 2002 2004 2006 2008 2010 2012

CHG_DET_BUNG

-.6

-.4

-.2

.0

.2

.4

.6

.8

1996 1998 2000 2002 2004 2006 2008 2010 2012

CHG_DET_HOUSE

-.16

-.12

-.08

-.04

.00

.04

.08

.12

.16

.20

.24

1996 1998 2000 2002 2004 2006 2008 2010 2012

CHG_SD_BUNG

-.15

-.10

-.05

.00

.05

.10

.15

.20

1996 1998 2000 2002 2004 2006 2008 2010 2012

CHG_SD_HOUSE

-.16

-.12

-.08

-.04

.00

.04

.08

.12

.16

.20

.24

1996 1998 2000 2002 2004 2006 2008 2010 2012

CHG_TERR

15

15.CorrelationsAPART DET_BUNG DET_HOUSE SD_BUNG SD_HOUSE TERRACE

APART  1.000000DET_BUNG  0.960163  1.000000DET_HOUSE  0.934838  0.966158  1.000000SD_BUNG  0.945578  0.990155  0.972522  1.000000SD_HOUSE  0.953541  0.989877  0.979734  0.992261  1.000000TERRACE  0.958442  0.988532  0.968624  0.992192  0.992716  1.000000CHG_APART CHG_DET_BU CHG_DET_HOU CHG_SD_BUNG CHG_SD_HOUSE CHG_TERRCHG_APART  1.000000CHG_DET_BUNG  0.454880  1.000000CHG_DET_HOUSE -0.059605 -0.163630  1.000000CHG_SD_BUNG  0.188009  0.398085  0.143698  1.000000CHG_SD_HOUSE  0.437971  0.698484  0.063578  0.438071  1.000000CHG_TERR  0.167476  0.480692  0.250597  0.524432  0.549279  1.000000

Chg Correlation

16

16.Decomposing to identify trends • Filters and Band-Pass Filters – unearth trends & cyclical components

– BK: symmetric approximation – no phase shifts – expense is trimming series – MA isolates frequencies applying MA to approximate band-pass filters constrained to produce stationary outcome

– H-P: smoothing method – minimization of variance – constrains variation of second order of growth component

– CF approach develops optimal finite-sample approximations for the band-pass filter• CF model random walk filter uses the whole time series for the calculation of each filtered

data point

• Cross-correlation – Level of correlation between market segments across time – lead/lag

• Granger- Causality – the causal nature of the relationships between the various house price cycles across time

• Trend/Cycle Decomposition – Regimes - examine and separate the data phases into regimes, allowing for lag-lead observations and trend relations and variations within given segments or time (trends/regimes)

17

17.Research Design • Stationarity ‘the future will be like the past’ • Inspection for all housing sectors using a

Correlogram show that on a price level basis the data is nonstationary temporally

• First differencing = – Data – integrated by an I(d) level of I(1)

• Also shows data to be of the AR form of AR(1)• First order AR(1):

18

18. Level & First differencing Date: 04/13/12 Time: 17:17Sample: 1995Q1 2012Q4Included observations: 66

Autocorrelation Partial Correlation AC   PAC  Q-Stat  Prob

      . |*******       . |******* 1 0.943 0.943 61.373 0.000      . |******|       . | . | 2 0.883 -0.049 116.09 0.000      . |******|       . | . | 3 0.828 0.002 164.89 0.000      . |******|       .*| . | 4 0.764 -0.104 207.14 0.000      . |***** |       .*| . | 5 0.692 -0.107 242.37 0.000      . |***** |       . | . | 6 0.623 -0.017 271.42 0.000      . |**** |       . | . | 7 0.556 -0.030 294.93 0.000      . |**** |       . | . | 8 0.489 -0.031 313.42 0.000      . |*** |       . |*. | 9 0.434 0.074 328.28 0.000      . |*** |       .*| . | 10 0.376 -0.084 339.60 0.000      . |** |       . | . | 11 0.319 -0.021 347.90 0.000      . |** |       . | . | 12 0.262 -0.058 353.63 0.000      . |** |       . | . | 13 0.213 0.012 357.48 0.000      . |*. |       . | . | 14 0.170 0.017 359.96 0.000      . |*. |       . | . | 15 0.136 0.055 361.59 0.000      . |*. |       . | . | 16 0.107 0.001 362.61 0.000      . |*. |       . | . | 17 0.076 -0.040 363.14 0.000      . | . |       . | . | 18 0.053 0.001 363.40 0.000      . | . |       . | . | 19 0.032 -0.011 363.50 0.000      . | . |       . |*. | 20 0.023 0.076 363.55 0.000      . | . |       .*| . | 21 0.005 -0.095 363.56 0.000      . | . |       . | . | 22 -0.008 0.022 363.56 0.000      . | . |       . | . | 23 -0.023 -0.048 363.62 0.000      . | . |       . |*. | 24 -0.022 0.122 363.67 0.000      . | . |       **| . | 25 -0.041 -0.213 363.86 0.000      . | . |       . | . | 26 -0.065 -0.034 364.33 0.000      .*| . |       . | . | 27 -0.085 -0.028 365.15 0.000      .*| . |       . |*. | 28 -0.096 0.108 366.24 0.000

Date: 04/13/12 Time: 17:18Sample: 1995Q1 2012Q4Included observations: 65

Autocorrelation Partial Correlation AC   PAC  Q-Stat  Prob

      . | . |       . | . | 1 0.022 0.022 0.0331 0.856      . | . |       . | . | 2 -0.051 -0.052 0.2148 0.898      . |*. |       . |*. | 3 0.099 0.102 0.9043 0.824      . |*. |       . |*. | 4 0.090 0.083 1.4845 0.829      . | . |       . | . | 5 -0.048 -0.043 1.6552 0.894      . | . |       . | . | 6 -0.009 -0.008 1.6611 0.948      . | . |       . | . | 7 -0.031 -0.053 1.7332 0.973      .*| . |       .*| . | 8 -0.138 -0.139 3.1909 0.922      . | . |       . |*. | 9 0.064 0.079 3.5101 0.941      . | . |       . | . | 10 -0.028 -0.038 3.5725 0.965      .*| . |       . | . | 11 -0.099 -0.060 4.3565 0.958      .*| . |       .*| . | 12 -0.102 -0.097 5.2110 0.951      . | . |       .*| . | 13 -0.045 -0.073 5.3780 0.966      .*| . |       . | . | 14 -0.074 -0.064 5.8461 0.970      . | . |       . | . | 15 -0.011 0.008 5.8558 0.982      .*| . |       .*| . | 16 -0.076 -0.081 6.3683 0.984      . | . |       . | . | 17 -0.059 -0.032 6.6854 0.987      . | . |       . | . | 18 -0.014 -0.042 6.7027 0.992      . | . |       . | . | 19 -0.021 -0.049 6.7425 0.995      . | . |       . | . | 20 0.068 0.068 7.1855 0.996      . | . |       .*| . | 21 -0.050 -0.068 7.4336 0.997      . | . |       . | . | 22 0.045 0.035 7.6399 0.998      .*| . |       .*| . | 23 -0.110 -0.158 8.8943 0.996      . |*. |       . |*. | 24 0.132 0.097 10.738 0.991      . | . |       . | . | 25 0.043 0.004 10.940 0.993      . | . |       . | . | 26 -0.044 -0.044 11.153 0.995      . | . |       .*| . | 27 -0.057 -0.083 11.524 0.996      . | . |       . | . | 28 0.016 -0.038 11.553 0.997

19

19.Cycle decomposition • Differencing shows NI market to be a random

walk of the form:where current price is an adaptive function of prior period ( ) disturbance term∓

• Constructs enable a deconstruction of observed price data into trend component (non-cycle) and cycle component

• Allows HP performance based on two different definitions of the business cycle as incorporated into band-pass filter modeling

20

20.Cycle decomposition2 • Generally the business cycle is identified as the stationary

component• An alternative definition of the business cycle relies on an

unobserved components (UC) view of an output:Yt = t + ct

• this construct allowing for differencing can also be stated in the Hodrick-Prescott (HP) filter where the time series is decomposed into a growth component (gYt; trend slope) and an additive cyclical component (cYt; disturbance measure), where:

yt = gYt + cYt • In the HP filter format, the growth component (gYt) is

constructed as the second difference.

21

21.Decomposition Analysis• Data separated into regimes, allowing for lead-lag

observations, trend relations & variations within given segments or time (trends/regimes)– Comparison of regime phases allows for the specification of

similarities & differences between market periods of growth & decline

– Temporal based regime segments are based on a quadratic structural form:

• ⍺ = intercept or non-temporal component of time, βit = data sensitivity measure to time; = data sensitivity measure to difference or change and vit is the standard error of time trend forecast

22

22.Decomposition Analysis2

• The it is omitted from the deterministic expression of the quadratic time trend that is compared to the actual price observations at each point in time to create the time-based market disturbance measures. This calculation is all the form:

T-t = ^yTt’ - yt or [+itt +itt2 - yt] • The T-t measure is thus compared to the forecast

disturbance of it to achieve a stochastic adjustment measure operating in any defined regime phase.

23

23.Regime segmentation

• Regime seg. Illustrates similar pricing growth and trend patterns across housing segments with variant, but associated cyclical disturbance terms– Summed regime segments compared with smoothing H-P filter– In boom phase spread in level of price & HP filter denote this

variance (this is key)– Spread is differentiated by the disturbance/cyclical comp.

• Allows for comparison of price estimates based on near-term rational expectations– Suggest a stochastic trend effect based upon adaptive

expectations (lagged AR construct)

24

24.AR(p) MA(q) • ARMA’s first component is the autoregressive or

AR term. The lagged value reflects the current market. An autoregressive model of order p, AR (p) has the form:

• The second component is the moving average (MA) term. With an MA, the forecasting model uses lagged values of the forecast error to improve the current forecast. he error can reflect any newly introduced shocks to current housing markets. The MA (q) has the form:

25

25.CF Filter• ARMA relations addressed using CF band filter• Employed given dominance of trend effects

operating across NI market• Full-term asymptotic (tends towards 0) model

a better fit for the data than BK approach

26

-50,000

-25,000

0

25,000

50,000

75,000

100,000

50,000

100,000

150,000

200,000

250,000

300,000

350,000

1996 1998 2000 2002 2004 2006 2008 2010 2012

DET_BUNG Trend Cycle

Hodrick-Prescott Filter (lambda=1600)

H_P_DTBUNG H_P_DTHOUSE

-80,000

-40,000

0

40,000

80,000

120,000

0

100,000

200,000

300,000

400,000

1996 1998 2000 2002 2004 2006 2008 2010 2012

DET_HOUSE Trend Cycle

Hodrick-Prescott Filter (lambda=1600)

H_P_SDHOUSE

-40,000

-20,000

0

20,000

40,000

60,000

80,000

0

50,000

100,000

150,000

200,000

250,000

1996 1998 2000 2002 2004 2006 2008 2010 2012

SD_HOUSE Trend Cycle

Hodrick-Prescott Filter (lambda=1600)

-20,000

0

20,000

40,000

60,000

0

50,000

100,000

150,000

200,000

250,000

1996 1998 2000 2002 2004 2006 2008 2010 2012

TERRACE Trend Cycle

Hodrick-Prescott Filter (lambda=1600)H_P_TERRACE

26.

27

80,000

100,000

120,000

140,000

160,000

180,000

200,000

220,000

1996 1998 2000 2002 2004 2006 2008 2010 2012

TERRF_REG0507Q2

80,000

100,000

120,000

140,000

160,000

180,000

200,000

220,000

1996 1998 2000 2002 2004 2006 2008 2010 2012

TERRF_REG0711Q2

20,000

40,000

60,000

80,000

100,000

120,000

1996 1998 2000 2002 2004 2006 2008 2010 2012

TERRF_REG9505Q2

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

1996 1998 2000 2002 2004 2006 2008 2010 2012

TERRF_REG9507Q2

0

40,000

80,000

120,000

160,000

200,000

240,000

1996 1998 2000 2002 2004 2006 2008 2010 2012

TERRF_REGLIN_9502Q2

28

28. CF – filter analysis

-40,000

-20,000

0

20,000

40,000

60,000

0

50,000

100,000

150,000

200,000

250,000

96 98 00 02 04 06 08 10 12

APART Non-cyclical Cycle

Asymmetric (time-varying) Filter

-50,000

0

50,000

100,000

150,000

200,000

250,000

1996 1998 2000 2002 2004 2006 2008 2010 2012

APARTAPART_NONCYC_CBPFILTER13_CF_APART

-60,000

-40,000

-20,000

0

20,000

40,000

60,000

80,000

100,000

1996 1998 2000 2002 2004 2006 2008 2010 2012

BPFILTER13_CF_APARTBPFILTER13_CF_DETBUNGBPFILTER13_CF_DETHSEBPFILTER13_CF_SDBUNGBPFILTER13_CF_SDHSEBPFILTER13_CF_TER

COMP_CF_DISTUR_GRAPH CORREL_CF_ERROR

-60,000

-40,000

-20,000

0

20,000

40,000

60,000

80,000

100,000

1996 1998 2000 2002 2004 2006 2008 2010 2012

BPFILTER13_CF_APARTBPFILTER13_CF_DETBUNGBPFILTER13_CF_DETHSEBPFILTER13_CF_SDBUNGBPFILTER13_CF_SDHSEBPFILTER13_CF_TER

29

29.Highlights of Approach and Results• Permits argument that sectors are segmented• Thus, trend (high correlations) are either a consequence of

interdependence of local housing markets/housing capital – lead by detached & semi-detached housing – (a trickle down preference for housing!)

• Non-Granger causality !!• Appears to be a contagion effect/impact here – higher

correlations during the boom and bust periods – not stable market conditions as (95-04)

• Contagion attributed to underwriting and financing – boom as well as bust– Usually a bust concept for segmented market with high correlations

30

30.What the paper does....

• Allows description of the trend & cycle components endogenous in the price time series

• Techniques enable in-depth investigation of trend patterns & endogenous cycle components

• Allows specification of stochastic pricing effects• Segmentation is shown to be ineffective by focusing

on both the error term & tracking error• Insights into disturbance measures – calculated from

the regime constructs & band pass filters

31

TE_CORREL

-100,000

-50,000

0

50,000

100,000

150,000

200,000

1996 1998 2000 2002 2004 2006 2008 2010 2012

TE_APART_DETBUNG TE_APT_DETHSETE_APART_SDBUNG TE_APART_SDHSETE_APART_TERR

-160,000

-120,000

-80,000

-40,000

0

40,000

80,000

120,000

160,000

200,000

1996 1998 2000 2002 2004 2006 2008 2010 2012

TE_APART_DETBUNG TE_APART_SDBUNGTE_APART_SDHSE TE_APART_TERRTE_APT_DETHSE TE_DEBUNG_SDBUNGTE_DEBUNG_SDHSE TE_DEBUNG_TERRTE_DETBUNG_DETHSE TE_SDBUNG_DETHSETE_SDBUNG_SDHSE TE_SDBUNG_TERRTE_SDHSE_DETHSE TE_SDHSE_TERR

TE_CORREL_APART

32

32.Key Findings• Lead period for detached property & semi-detached

operate as price leaders• Lag period for apartments!!! Surprising!!!• All other housing tend to cluster and lag

– Trickle down effect of housing wealth into other types!• Endogenous market distorted (segmented) by

external forces– Signals

• Contagion effect (behavioural) hot + cold markets• By type magnitude differs – heterogeneous choice• External factor causes similar expectations (boom and bust)

financial tailoring; liberalisation

33

33.Key findings• Dominance of trend relationships measured &

specified by comparing regime segments with HP calculated trends

• HP filter– reflects adaptive expectations & behaviour in pricing

• A story evident relating to diff market conditions (B/B)• Moving forward – associations lead us into quantifying

B/B and bubbles• Regimes – exogenous measure of risk• Endogenous measure of risk (BP)

34

34.Conclusions• Decomposition of pricing behaviour into trend/cyclic component shows

segmentation and distinctive markets– Segmentation infers that interdependence is a result of contagion effects– Supported by recognition of lags– Contagion dominates during boom/bust periods

• Regardless of impact of contagion for interdependency on pricing behaviour a deterministic/stochastic trend effect dominates pricing across typologies – This suggested deterministic trend (non stationary - time sensitive trends and

stationary disturbance - random walk cycle components - with form AR(1) and I(d)/I(1)• Segmentation endogenously operating in the price structure based on

differences in the cycle/disturbance components with first differencing adjustment (1 to 2 period lags) in operation

• Nonstationary cyclical component based on unit root factor & differencing requirement supports:– Adaptive lag pricing and random walk construct– Allows NI market to be structured & forecast based on deterministic trend component

& stationary cycle effect